arXiv：cs.AI· Jiaqi Wang, Wei Chen, Xiaoyu Li, Lexian Xia, ShuaiLong Tang, Chenran Zhang, Donglin Xie, Guangpu Zhu, Yelei Li, Yi Zhou·· 16 小时前
HiPPT：基于多范围文本对齐的生理学基础可穿戴 PPG 表征学习
HiPPT: Physiology-Grounded Wearable PPG Representation Learning via Multi-Scope Text Alignment
AI 导读
研究者提出 HiPPT，一个将每个监督来源匹配到合适证据范围的生理学基础框架，用于可穿戴 PPG 表征学习。他们构建了包含 202K 用户、超 400 万条腕戴记录及波形形态与元数据描述的 PPG-Text 语料，结合记录级形态对齐与跨记录上下文对齐，并引入波形引导文本建模。
正文
Abstract:Reusable wearable PPG representations should capture local pulse morphology and broader physiological regularities. Existing pretraining objectives, however, usually apply supervision at the recording or pair level, overlooking that different concepts become reliable over different scopes of waveform evidence. We propose HiPPT, a physiology-grounded framework that matches each supervision source to its appropriate evidence scope. We construct a PPG-Text corpus of over 4 million wrist-worn recordings from 202K users, pairing recordings with waveform-derived morphology and metadata-derived context descriptions. HiPPT combines record-level morphology alignment, which grounds individual recordings in pulse characteristics, with cross-recording context alignment, which aggregates recordings sharing the same metadata tuple to capture context-consistent regularities. Waveform-guided text modeling further recovers masked contextual concepts from PPG patch representations, providing fine-grained waveform-text supervision. Across six downstream tasks spanning blood pressure, vascular stiffness, physiological attributes, and cross-dataset heart-rate estimation, HiPPT achieves the best result on the primary linear-probing metrics. The aligned space also supports zero-shot profiling of age group, sex, and BMI category, individually and jointly, with performance improving as more recordings are aggregated. These results show that evidence-scope matching yields transferable and semantically accessible wearable PPG representations. Code and pretrained weights will be released.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14489 [cs.AI] |
| (or arXiv:2610.14489v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14489 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiaqi Wang [view email]
[v1]
Sat, 10 Oct 2026 02:38:33 UTC (2,840 KB)
来源：arXiv：cs.AI · arxiv.org